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1308 | 1 | null | null | 31 | 11703 | In the traditional Birthday Paradox the question is "what are the chances that two or more people in a group of $n$ people share a birthday". I'm stuck on a problem which is an extension of this.
Instead of knowing the probability that two people share a birthday, I need to extend the question to know what is the prob... | Extending the birthday paradox to more than 2 people | CC BY-SA 3.0 | null | 2010-08-05T14:00:07.723 | 2016-10-14T21:17:41.107 | 2016-10-14T21:17:41.107 | 919 | 765 | [
"probability",
"combinatorics",
"birthday-paradox"
] |
1309 | 2 | null | 1308 | 2 | null | It is always possible to solve this problem with a monte-carlo solution, although that's far from the most efficient. Here's a simple example of the 2 person problem in R (from [a presentation I gave last year](http://www.meetup.com/nyhackr/calendar/10251302/?from=list&offset=0); I used this as an example of inefficie... | null | CC BY-SA 2.5 | null | 2010-08-05T14:10:26.907 | 2010-08-05T14:54:11.593 | 2010-08-05T14:54:11.593 | 5 | 5 | null |
1311 | 2 | null | 1308 | 1 | null | This is an attempt at a general solution. There may be some mistakes so use with caution!
First some notation:
$P(x,n)$ be the probability that $x$ or more people share a birthday among $n$ people,
$P(y|n)$ be the probability that exactly $y$ people share a birthday among $n$ people.
Notes:
- Abuse of notation as $P(.... | null | CC BY-SA 2.5 | null | 2010-08-05T15:27:49.650 | 2010-08-05T15:34:26.243 | 2010-08-05T15:34:26.243 | null | null | null |
1312 | 2 | null | 1257 | 2 | null | It depends on exactly what you mean - your notation is a bit ambiguous. If I understand your notation correctly, you don't actually have 4 independent random variables - $X_3$ is a deterministic function of $Y_1$ and $Y_2$, and so it shouldn't occur explicitly in the likelihood. $Y_3$ is a function of the deterministic... | null | CC BY-SA 2.5 | null | 2010-08-05T16:04:34.100 | 2010-08-05T16:04:34.100 | null | null | 643 | null |
1313 | 2 | null | 1278 | 4 | null | If you cannot collect data on a different ward where you don't do the intervention, your conclusions will be weak, because you cannot rule out other causes that act simultaneously (change in weather, season, epidemic of something, etc, etc). However if you observe a large effect, your study would still contribute an in... | null | CC BY-SA 2.5 | null | 2010-08-05T16:54:35.073 | 2010-08-05T16:54:35.073 | null | null | 279 | null |
1315 | 1 | null | null | 22 | 6491 | I've sampled a real world process, network ping times. The "round-trip-time" is measured in milliseconds. Results are plotted in a histogram:
[](https://i.stack.imgur.com/9fL76.png)
[](https://i.stack.imgur.com/Jy5No.png)
Latency has a minimum value, but a long upper tail.
I want to know what statistical distribution t... | How do I figure out what kind of distribution represents this data on ping response times? | CC BY-SA 4.0 | null | 2010-08-05T19:26:34.060 | 2023-04-06T17:42:33.837 | 2022-03-08T04:42:19.087 | 775 | 775 | [
"distributions",
"sample-size",
"sample",
"normality-assumption",
"distribution-identification"
] |
1316 | 2 | null | 1315 | 4 | null | Try the [gamma](http://en.wikipedia.org/wiki/Gamma_distribution) distribution which is parametrized as $x \sim Gamma(k,\theta)$. If you see these [pdf plots](http://en.wikipedia.org/wiki/File%3aGamma_distribution_pdf.svg) of the gamma from the wiki you will see that there are some plots that look similar to what you ha... | null | CC BY-SA 2.5 | null | 2010-08-05T19:33:56.103 | 2010-08-05T19:51:03.543 | 2010-08-05T19:51:03.543 | null | null | null |
1317 | 2 | null | 726 | 9 | null | 9 out of ten dentists think the 10th dentist is an idiot.
- No idea who said it.
| null | CC BY-SA 2.5 | null | 2010-08-05T19:41:53.000 | 2010-08-05T19:41:53.000 | null | null | 776 | null |
1318 | 2 | null | 1315 | 8 | null | There is no reason to expect that any real world data set will fit a known distributional form...especially from such a known messy data source.
What you want to do with the answers will largely indicate an approach. For example, if you want to know when the ping times have changed significantly, then trending the empi... | null | CC BY-SA 2.5 | null | 2010-08-05T19:51:02.640 | 2010-08-05T19:51:02.640 | null | null | 247 | null |
1319 | 2 | null | 1315 | 11 | null | Let me ask a more basic question: what do you want to do with this distributional information?
The reason I ask is because it may well make more sense to approximate the distribution with some sort of kernel density estimator, rather than insist that it fit into one of the (possibly shifted) exponential family distrib... | null | CC BY-SA 2.5 | null | 2010-08-05T20:13:15.683 | 2010-08-05T20:13:15.683 | null | null | 61 | null |
1320 | 2 | null | 726 | 5 | null | No statistican, but useful for the profession:
>
The perfect is the enemy of the good
- Voltaire
| null | CC BY-SA 2.5 | null | 2010-08-05T20:38:41.187 | 2010-08-05T20:38:41.187 | null | null | 778 | null |
1321 | 1 | 1322 | null | 7 | 2806 | What would be the best way to display changes in two scalar variables (x,y) over time (z), in one visualization?
One idea that I had was to plot x and y both on the vertical axis, with z as the horizontal.
Note: I'll be using R and likely ggplot2
| Visualizing two scalar variables over time | CC BY-SA 2.5 | null | 2010-08-05T21:12:50.063 | 2011-08-18T20:30:22.123 | 2010-11-30T16:43:14.843 | 8 | 776 | [
"r",
"time-series",
"data-visualization",
"ggplot2"
] |
1322 | 2 | null | 1321 | 7 | null | The other idea is to plot one series as x and the second as y -- the time dependency will be hidden, but this plots shows correlations pretty well. (Yet time can be shown to some extent by connecting points chronologically; if the series are quite short and continuous it should be readable.)
| null | CC BY-SA 2.5 | null | 2010-08-05T22:01:03.533 | 2010-08-05T22:01:03.533 | null | null | null | null |
1324 | 1 | 1326 | null | 5 | 443 | The title is quite self-explanatory - I'd like to know if there's any other parametric technique apart from repeated-measures ANOVA, that can be utilized in order to compare several (more than 2) repeated measures?
| Parametric techniques for n-related samples | CC BY-SA 2.5 | null | 2010-08-05T22:16:20.007 | 2010-09-16T07:04:59.787 | 2010-09-16T07:04:59.787 | null | 1356 | [
"repeated-measures"
] |
1325 | 2 | null | 1315 | 13 | null | Weibull is sometimes used for modelling ping time. try a weibull distribution. To fit one in R:
```
x<-rweibull(n=1000,shape=2,scale=100)
#generate a weibull (this should be your data).
hist(x)
#this is an histogram of your data.
library(survival)
a1<-survreg(Surv(x,rep(1,1000))~1,dist='weibull')
exp(a1$coef) #this is ... | null | CC BY-SA 2.5 | null | 2010-08-05T22:17:29.820 | 2010-08-06T16:20:50.017 | 2010-08-06T16:20:50.017 | 603 | 603 | null |
1326 | 2 | null | 1324 | 8 | null | Multilevel/hierarchical linear models can be used for this. Essentially, each repetition of the measure is clustered within the individual; individuals can then be clustered within other hierarchies. For me, at least, it's more intuitive than repeated-measures ANOVA.
The canonical text is [Raudenbush and Bryk](http:/... | null | CC BY-SA 2.5 | null | 2010-08-05T22:35:46.630 | 2010-08-05T23:06:58.227 | 2010-08-05T23:06:58.227 | 71 | 71 | null |
1327 | 2 | null | 1315 | 3 | null | Looking at it I would say a skew-normal or possibly a binormal distribution may fit it well.
In R you could use the `sn` library to deal with skew-normal distribution and use `nls` or `mle` to do a non-linear least square or a maximum likelihood extimation fit of your data.
===
EDIT: rereading your question/comments I ... | null | CC BY-SA 2.5 | null | 2010-08-05T23:22:01.933 | 2010-08-06T06:19:32.700 | 2010-08-06T06:19:32.700 | 582 | 582 | null |
1328 | 2 | null | 726 | 12 | null | "If you think that statistics has nothing to say about what you do or how you could do it better, then you are either wrong or in need of a more interesting job." - Stephen Senn (Dicing with Death: Chance, Risk and Health, Cambridge University Press, 2003)
| null | CC BY-SA 2.5 | null | 2010-08-06T00:29:25.567 | 2010-08-06T00:29:25.567 | null | null | 781 | null |
1330 | 2 | null | 726 | 77 | null | >
The best thing about being a statistician is that you get to play in everyone's backyard.
-- John Tukey
(This is MY favourite Tukey quote)
| null | CC BY-SA 2.5 | null | 2010-08-06T01:08:05.617 | 2010-12-03T04:02:12.130 | 2010-12-03T04:02:12.130 | 795 | 521 | null |
1331 | 2 | null | 485 | 5 | null | There is a new resources forming these days for talks about R:
[https://www.r-bloggers.com/RUG/](https://www.r-bloggers.com/RUG/)
Compiled by the organizers of "R Users Groups" around the world (right now, mainly around the States).
It is a new project (just a few weeks old), but already got good content on it, and goo... | null | CC BY-SA 4.0 | null | 2010-08-06T01:15:52.747 | 2022-12-31T07:32:40.610 | 2022-12-31T07:32:40.610 | 79696 | 253 | null |
1332 | 2 | null | 726 | 30 | null | "It is easy to lie with statistics. It is hard to tell the truth without statistics." - Andrejs Dunkels
| null | CC BY-SA 2.5 | null | 2010-08-06T01:20:36.700 | 2010-08-06T01:20:36.700 | null | null | 521 | null |
1333 | 2 | null | 726 | 137 | null | >
Statisticians, like artists, have the bad habit of falling in love with their models.
-- George Box
| null | CC BY-SA 2.5 | null | 2010-08-06T01:26:54.020 | 2010-10-02T17:10:08.093 | 2010-10-02T17:10:08.093 | 795 | 521 | null |
1334 | 2 | null | 726 | 12 | null | >
"New methods always look better than old ones. Neural nets are better
than logistic regression, support vector machines are better than
neural nets, etc." - Brad Efron
| null | CC BY-SA 3.0 | null | 2010-08-06T01:29:56.867 | 2018-02-11T16:09:54.783 | 2018-02-11T16:09:54.783 | 22387 | 521 | null |
1335 | 2 | null | 1286 | 6 | null | If you wish to trade processing speed for memory (which I think you do), I would suggest the following algorithm:
- Set up a loop from 1 to N Choose K, indexed by i
- Each i can be considered an index to a combinadic, decode as such
- Use the combination to perform your test statistic, store the result, discard the ... | null | CC BY-SA 2.5 | null | 2010-08-06T01:40:54.227 | 2010-08-06T01:40:54.227 | null | null | 729 | null |
1336 | 2 | null | 726 | 12 | null | >
In the long run, we're all dead.
-- John Maynard Keynes.
A reference to survival analysis?!
| null | CC BY-SA 2.5 | null | 2010-08-06T01:43:46.623 | 2010-12-03T04:05:41.940 | 2010-12-03T04:05:41.940 | 795 | 521 | null |
1337 | 1 | null | null | 186 | 254626 | Well, we've got favourite statistics quotes. What about statistics jokes?
| Statistics Jokes | CC BY-SA 3.0 | null | 2010-08-06T01:53:47.023 | 2021-10-23T10:39:14.333 | 2018-03-08T17:43:38.810 | 2669 | 521 | [
"references",
"humor"
] |
1338 | 2 | null | 1337 | 45 | null | I thought I'd start the ball rolling with my favourite.
"Being a statistician means never having to say you are certain."
| null | CC BY-SA 2.5 | null | 2010-08-06T01:54:55.680 | 2010-08-06T01:54:55.680 | null | null | 521 | null |
1339 | 2 | null | 1207 | 4 | null | You may want to define what you want more clearly (to yourself, if not here). If what you're looking for is the most statistically significant stationary period contained in your noisy data, there's essentially two routes to take:
1) compute a robust autocorrelation estimate, and take the maximum coefficient
2) compute... | null | CC BY-SA 2.5 | null | 2010-08-06T02:48:09.630 | 2010-08-06T02:48:09.630 | null | null | 781 | null |
1340 | 2 | null | 1164 | 23 | null | Anyone trained in statistical data analysis at a reasonable level uses the concepts of robust statistics on a regular basis. Most researchers know enough to look for serious outliers and data recording errors; the policy of removing suspect data points goes back well into the 19th century with Lord Rayleigh, G.G. Stoke... | null | CC BY-SA 2.5 | null | 2010-08-06T03:06:41.747 | 2010-08-06T15:49:38.253 | 2010-08-06T15:49:38.253 | 781 | 781 | null |
1341 | 2 | null | 652 | 4 | null | The classic "orange horror" remains an excellent introduction: Exploratory Data Analysis by John Tukey.
[http://www.amazon.com/Exploratory-Data-Analysis-John-Tukey/dp/0201076160](http://rads.stackoverflow.com/amzn/click/0201076160)
| null | CC BY-SA 2.5 | null | 2010-08-06T03:14:53.140 | 2010-08-06T03:14:53.140 | null | null | 781 | null |
1342 | 2 | null | 726 | 4 | null | >
Do not make things easy for yourself
by speaking or thinking of data as if
they were different from what they
are; and do not go off from facing
data as they are, to amuse your
imagination by wishing they were
different from what they are. Such
wishing is pure waste of nerve force,
weakens your intel... | null | CC BY-SA 2.5 | null | 2010-08-06T03:17:17.273 | 2010-08-06T03:17:17.273 | null | null | null | null |
1343 | 2 | null | 1321 | 6 | null | I sometimes make the x-axis time and plot both scalar variables on the y-axis.
When the two scalar variables are on a different metric, I rescale one or both of the scalar variables so they can be displayed on the same plot.
I use things like colour and shape to discriminate the two scalar variables.
I've often used `x... | null | CC BY-SA 2.5 | null | 2010-08-06T03:22:13.263 | 2010-08-06T03:22:13.263 | null | null | 183 | null |
1344 | 2 | null | 1315 | 6 | null | A simpler approach might be to transform the data. After transforming, it might be close to Gaussian.
One common way to do so is by taking the logarithm of all values.
My guess is that in this case the distribution of the reciprocal of the round-trip times will be more symmetrical and perhaps close to Gaussian. By ta... | null | CC BY-SA 2.5 | null | 2010-08-06T03:47:26.320 | 2010-08-06T03:47:26.320 | null | null | 25 | null |
1345 | 2 | null | 1315 | 2 | null | Based on your comment "Really i want to draw the mathematical curve that follows the distribution. Granted it might not be a known distribution; but i can't imagine that this hasn't been investigated before." I am providing a function that sort of fits.
Take a look at [ExtremeValueDistribution](http://reference.wolfram... | null | CC BY-SA 2.5 | null | 2010-08-06T04:11:29.997 | 2010-08-06T04:11:29.997 | null | null | 782 | null |
1346 | 2 | null | 1337 | 140 | null | I saw this posted as a comment on here somewhere:
[http://xkcd.com/552/](http://xkcd.com/552/)
A: I used to think correlation implied causation. Then I took a statistics class. Now I don't.
B: Sounds like the class helped.
A: Well, maybe.
Title text: Correlation... | null | CC BY-SA 3.0 | null | 2010-08-06T04:50:59.280 | 2014-08-29T04:17:00.007 | 2020-06-11T14:32:37.003 | -1 | 287 | null |
1347 | 2 | null | 726 | 19 | null | "Extraordinary claims demand extraordinary evidence."
Often attributed to Carl Sagan, but he was paraphrasing sceptic Marcello Truzzi. Doubtless the concept is even more ancient.
David Hume said, "A wise man, therefore, proportions his belief to the evidence".
One could argue this is not a quote about statistics. Ho... | null | CC BY-SA 2.5 | null | 2010-08-06T05:15:22.583 | 2010-08-06T05:15:22.583 | null | null | 521 | null |
1348 | 2 | null | 652 | 1 | null | An old favourite of mine as an introduction to biostatistics is Armitage & Berry's (& now Matthew's):
Statistical Methods in Medical Research
| null | CC BY-SA 2.5 | null | 2010-08-06T05:27:57.743 | 2010-08-06T05:27:57.743 | null | null | 521 | null |
1349 | 2 | null | 1252 | 22 | null | I have previously found UCLA's "Choosing the Correct Statistical Test" to be helpful:
[https://stats.idre.ucla.edu/other/mult-pkg/whatstat/](https://stats.idre.ucla.edu/other/mult-pkg/whatstat/)
It also gives examples of how to do the analysis in SAS, Stata, SPSS and R.
| null | CC BY-SA 4.0 | null | 2010-08-06T05:33:06.860 | 2020-03-04T23:41:10.463 | 2020-03-04T23:41:10.463 | 113546 | 521 | null |
1350 | 1 | 1407 | null | 2 | 4184 | I am working with a large data set (approximately 50K observations) and trying to running a Maximum likelihood estimation on 5 unknowns in Stata.
I encountered an error message of "Numerical Overflow". How can I overcome this?
I am trying to run a Stochastic Frontier analysis using the built in Stata command "frontie... | How to get around Numerical Overflow in Stata? | CC BY-SA 2.5 | null | 2010-08-06T06:32:06.513 | 2010-10-08T16:07:49.240 | 2010-10-08T16:07:49.240 | 8 | 189 | [
"large-data",
"stata",
"computational-statistics"
] |
1351 | 2 | null | 1296 | 5 | null | Assuming you want to pick a distribution for n, p(n) you can apply Bayes law.
You know that the probability of k events occuring given that n have actually occured is governed by a binomial distribtion
$p(k|n) = {n \choose k} p^k (1-p)^{(n-k)}$
The thing you really want to know is the probability of n events having ac... | null | CC BY-SA 2.5 | null | 2010-08-06T07:44:34.903 | 2010-08-06T07:44:34.903 | null | null | 789 | null |
1352 | 1 | 1384 | null | 11 | 3570 | In an average (median?) conversation about statistics you will often find yourself discussing this or that method of analyzing this or that type of data. In my experience, careful study design with special thought with regards to the statistical analysis is often neglected (working in biology/ecology, this seems to be ... | References for how to plan a study | CC BY-SA 2.5 | null | 2010-08-06T08:06:13.193 | 2017-03-06T08:11:55.837 | 2010-09-16T06:58:05.970 | null | 144 | [
"experiment-design"
] |
1353 | 2 | null | 1296 | 12 | null | I would choose to use the [negative binomial distribution](http://www.math.ntu.edu.tw/~hchen/teaching/StatInference/notes/lecture16.pdf), which returns the probability that there will be X failures before the k_th success, when the constant probability of a success is p.
Using an example
```
k=17 # number of successes
... | null | CC BY-SA 2.5 | null | 2010-08-06T09:01:29.013 | 2010-08-06T10:55:02.630 | 2010-08-06T10:55:02.630 | 339 | 339 | null |
1354 | 2 | null | 1350 | 1 | null | How many variables do you have? Are you using a built in command?
Five years ago I used mlogit (multinomial logit) in Stata 8 to model 60,000 observations and about 40 variables with no difficulty.
I'd recommend searching the Stata websites (http://www.stata.com/) and even asking technical support.
For example, here a... | null | CC BY-SA 2.5 | null | 2010-08-06T09:34:37.720 | 2010-08-06T09:34:37.720 | null | null | 521 | null |
1355 | 1 | 1364 | null | 8 | 3283 | I want to predict the results of a simple card game, to judge on average, how long a game will last.
My 'simple' game is;
- Cards are dealt from a randomised
deck to n players (typically 2-4)
- Each player gets five cards
- The top
card from the deck is turned over
- Each player takes it in turns to
either place a ... | How could I predict the results of a simple card game? | CC BY-SA 2.5 | null | 2010-08-06T09:48:14.730 | 2016-06-03T07:21:02.127 | 2010-08-06T10:01:52.910 | 8 | 114 | [
"r",
"probability",
"games"
] |
1356 | 2 | null | 1337 | 113 | null | Two statisticians were traveling in an airplane from LA to New York.
About an hour into the flight, the pilot announced that they had lost
an engine, but don’t worry, there are three left. However, instead of 5
hours it would take 7 hours to get to New York.
A little later, he
announced that a second engine failed, an... | null | CC BY-SA 2.5 | null | 2010-08-06T09:52:51.527 | 2010-08-06T09:52:51.527 | null | null | 114 | null |
1357 | 1 | null | null | 4 | 2877 | I am trying to compare it to Euclidean distance and Pearson correlation
| Is mutual information invariant to scaling, i.e. multiplying all elements by a nonzero constant? | CC BY-SA 2.5 | null | 2010-08-06T10:48:52.543 | 2011-04-29T00:26:49.170 | 2011-04-29T00:26:49.170 | 3911 | null | [
"correlation",
"mutual-information"
] |
1358 | 1 | 1365 | null | 14 | 989 | In circular statistics, the expectation value of a random variable $Z$ with values on the circle $S$ is defined as
$$
m_1(Z)=\int_S z P^Z(\theta)\textrm{d}\theta
$$
(see [wikipedia](http://en.wikipedia.org/wiki/Circular_statistics#Moments)).
This is a very natural definition, as is the definition of the variance
$$
\ma... | Intuition for higher moments in circular statistics | CC BY-SA 2.5 | null | 2010-08-06T10:57:18.820 | 2011-04-29T00:27:53.567 | 2011-04-29T00:27:53.567 | 3911 | 650 | [
"mathematical-statistics",
"moments",
"intuition",
"circular-statistics"
] |
1359 | 2 | null | 1352 | 3 | null | In general, I would say any book that has DOE (design of experiments) in the title would fit the bill (and there are MANY).
My rule of thumb for such resource would be to start with the [wiki page](http://en.wikipedia.org/wiki/Design_of_experiments), in particular to your question, notice the [Principles of experimenta... | null | CC BY-SA 2.5 | null | 2010-08-06T11:09:39.877 | 2010-08-06T11:09:39.877 | null | null | 253 | null |
1360 | 2 | null | 726 | 36 | null | >
Those who ignore Statistics are condemned to reinvent it.
-- Brad Efron
| null | CC BY-SA 2.5 | null | 2010-08-06T11:11:00.890 | 2010-12-03T04:03:04.030 | 2010-12-03T04:03:04.030 | 795 | 778 | null |
1361 | 2 | null | 726 | 20 | null | >
My thesis is simply this: probability does not exist.
- Bruno de Finetti
| null | CC BY-SA 2.5 | null | 2010-08-06T11:15:17.060 | 2010-08-06T11:15:17.060 | null | null | 778 | null |
1362 | 2 | null | 1352 | 3 | null | My rule of thumb is "repeat more than you think it's sufficient".
| null | CC BY-SA 2.5 | null | 2010-08-06T11:17:09.273 | 2010-08-06T15:23:24.753 | 2010-08-06T15:23:24.753 | null | null | null |
1363 | 2 | null | 726 | 2 | null | >
...Statistics used as a catalyst to engineering creation will, I believe, always result in the fastest and most economical progress.
--George Box 1992
| null | CC BY-SA 2.5 | null | 2010-08-06T11:23:15.363 | 2010-12-03T04:03:46.730 | 2010-12-03T04:03:46.730 | 795 | 114 | null |
1364 | 2 | null | 1355 | 11 | null | The easiest way is just to simulate the game lots of times. The R code below simulates a single game.
```
nplayers = 4
#Create an empty data frame to keep track
#of card number, suit and if it's magic
empty.hand = data.frame(number = numeric(52),
suit = numeric(52),
magic = numeric(52))
#A list of players who are... | null | CC BY-SA 2.5 | null | 2010-08-06T12:13:05.263 | 2010-08-09T16:31:07.027 | 2010-08-09T16:31:07.027 | 8 | 8 | null |
1365 | 2 | null | 1358 | 9 | null | The moments are the Fourier coefficients of the probability measure $P^Z$. Suppose (for the sake of intuition) that $Z$ has a density. Then the argument (angle from $1$ in the complex plane) of $Z$ has a density on $[0,2\pi)$, and the moments are the coefficients when that density is expanded in a Fourier series. Th... | null | CC BY-SA 2.5 | null | 2010-08-06T12:38:35.230 | 2010-08-06T12:38:35.230 | null | null | 89 | null |
1366 | 2 | null | 1315 | 4 | null | Another approach, that is more justified by network considerations, is to try to fit a sum of independent exponentials with different parameters. A reasonable assumption would be that each node in the path of the ping the delay would be an independent exponential, with different parameters. A reference to the distribut... | null | CC BY-SA 2.5 | null | 2010-08-06T12:46:57.243 | 2010-08-06T12:46:57.243 | null | null | 247 | null |
1367 | 2 | null | 1337 | 96 | null | One passed by Gary Ramseyer:
Statistics play an important role in genetics. For instance, statistics prove that numbers of offspring is an inherited trait. If your parent didn't have any kids, odds are you won't either.
| null | CC BY-SA 2.5 | null | 2010-08-06T13:52:50.747 | 2010-08-06T13:52:50.747 | null | null | 634 | null |
1368 | 2 | null | 1337 | 21 | null | A statistic professor plans to travel to a conference by plane. When he passes the security check, they discover a bomb in his carry-on-baggage. Of course, he is hauled off immediately for interrogation.
"I don't understand it!" the interrogating officer exclaims. "You're an accomplished professional, a caring family m... | null | CC BY-SA 2.5 | null | 2010-08-06T14:00:40.623 | 2010-08-06T14:00:40.623 | null | null | null | null |
1369 | 1 | 1372 | null | 0 | 208 | I have a given distance with a standard deviation. I have simulated now a few 100 distances and would like to draw from these distances a sample of 10-20 resembling the original distribution. Is there any standardized way of doing so?
| Sampling according to a normal distribution | CC BY-SA 2.5 | null | 2010-08-06T14:01:57.110 | 2010-08-06T15:14:46.647 | 2010-08-06T14:14:37.210 | 791 | 791 | [
"sample"
] |
1370 | 2 | null | 1357 | 7 | null | I think the answer is yes to your question. I will show this for the discrete case only and I think the basic idea carries over to the continuous case. MI is defined as:
$I(X;Y) = \sum_{y\in Y}\sum_{x\in X}\Bigg(p(x,y) log(\frac{p(x,y)}{p(x)p(y)})\Bigg)$
Define:
$Z_x = \alpha X$
and
$Z_y = \alpha Y$.
So, the question ... | null | CC BY-SA 2.5 | null | 2010-08-06T14:10:01.230 | 2010-08-06T14:10:01.230 | null | null | null | null |
1371 | 2 | null | 1337 | 137 | null | George Burns said that "If you live to be one hundred, you've got it made. Very few people die past that age."
| null | CC BY-SA 2.5 | null | 2010-08-06T14:12:21.147 | 2010-08-06T14:12:21.147 | null | null | 666 | null |
1372 | 2 | null | 1369 | 2 | null | You mean you want to draw 10-20 numbers from a normal distribution? In R, use `rnorm` function; for a generic solution, see [Wikipedia](http://en.wikipedia.org/wiki/Normal_distribution#Generating_values_from_normal_distribution).
| null | CC BY-SA 2.5 | null | 2010-08-06T15:14:46.647 | 2010-08-06T15:14:46.647 | null | null | null | null |
1373 | 2 | null | 1357 | 1 | null | Intuitive explanation is such: multiplying by constant does not change information content of X and Y, so also their mutual information -- and thus it is invariant to scaling. Still Srikant gave you a strict proof of this fact.
| null | CC BY-SA 2.5 | null | 2010-08-06T15:22:43.663 | 2010-08-06T15:22:43.663 | null | null | null | null |
1374 | 2 | null | 1337 | 11 | null | How many statisticians does it take to change a light bulb?
5–7, with p-value 0.01
| null | CC BY-SA 2.5 | null | 2010-08-06T16:10:48.470 | 2010-08-06T17:54:34.830 | 2010-08-06T17:54:34.830 | null | null | null |
1375 | 2 | null | 1337 | 34 | null | Here is a list of many fun statistics jokes ([link](http://www.se16.info/hgb/statjoke.htm))
Here are just a few:
---
Did you hear the one about the statistician? Probably....
---
It is proven that the celebration of birthdays is healthy. Statistics show that those people who celebrate the most birthdays become th... | null | CC BY-SA 3.0 | null | 2010-08-06T18:42:26.487 | 2013-06-27T19:56:07.393 | 2013-06-27T19:56:07.393 | 6981 | 253 | null |
1376 | 1 | 1397 | null | 6 | 1036 | I am looking for a robust version of Hotelling's $T^2$ test for the mean of a vector. As data, I have a $m\ \times\ n$ matrix, $X$, each row an i.i.d. sample of an $n$-dimensional RV, $x$. The null hypothesis I wish to test is $E[x] = \mu$, where $\mu$ is a fixed $n$-dimensional vector. The classical Hotelling test ap... | Robust version of Hotelling $T^2$ test | CC BY-SA 2.5 | null | 2010-08-06T19:02:08.477 | 2022-12-11T10:13:47.333 | 2010-09-16T06:57:55.643 | null | 795 | [
"robust"
] |
1377 | 2 | null | 1337 | 68 | null | "If you torture data enough it will confess" one of my professors
| null | CC BY-SA 2.5 | null | 2010-08-06T20:07:17.070 | 2010-08-06T20:07:17.070 | null | null | 236 | null |
1378 | 1 | null | null | 10 | 1157 | I have a dataset that contains ~7,500 blood tests from ~2,500 individuals. I'm trying to find out if variability in the blood tests increases or decreases with the time between two tests. For example - I draw your blood for the baseline test, then immediately draw a second sample. Six months later, I draw another sa... | Estimating variability over time | CC BY-SA 2.5 | null | 2010-08-06T21:54:11.230 | 2010-08-17T06:29:42.563 | null | null | 71 | [
"repeated-measures",
"variability"
] |
1379 | 2 | null | 1202 | 1 | null | you could compute a [Kolmogorov-Smirnov](http://en.wikipedia.org/wiki/Kolmogorov_Smirnov_Test) statistic based on your binned data. This would work by first computing an empirical CDF based on your bins (just a cumulative sum with rescaling), then compute the $\infty$-norm of the differences.
I don't know R well enough... | null | CC BY-SA 2.5 | null | 2010-08-06T22:10:44.290 | 2010-08-07T16:00:25.753 | 2010-08-07T16:00:25.753 | 795 | 795 | null |
1380 | 1 | 1636 | null | 3 | 2734 | (migrating from math overflow, where no answers were posted)
suppose I have $K$ different methods for forecasting a binary random variable, which I test on independent sets of data, resulting in $K$ contingency tables of values $n_{ijk}$ for $i,j=1,2$ and $k=1,2,...,K$. How can I compare these methods based on the cont... | Comparing multiple contingency tables, independent data | CC BY-SA 2.5 | null | 2010-08-06T22:16:01.897 | 2010-10-01T01:34:16.313 | 2010-09-30T21:20:58.353 | 930 | 795 | [
"forecasting",
"contingency-tables"
] |
1381 | 2 | null | 1001 | 5 | null | The Baumgartner-Weiss-Schindler statistic is a modern alternative to the K-S test, and appears to be more powerful in certain situations. A few links:
- A Nonparametric Test for the General Two-Sample Problem (the original B.W.S. paper)
- M. Neuhauser, 'Exact Tests Based on the Baumgartner-Weiss-Schindler Statistic--... | null | CC BY-SA 4.0 | null | 2010-08-06T22:34:27.163 | 2022-04-17T17:42:24.263 | 2022-04-17T17:42:24.263 | 79696 | 795 | null |
1382 | 2 | null | 1337 | 78 | null | A statistics major was completely hung over the day of his final exam. It was a true/false test, so he decided to flip a coin for the answers. The statistics professor watched the student the entire two hours as he was flipping the coin … writing the answer … flipping the coin … writing the answer. At the end of the tw... | null | CC BY-SA 2.5 | null | 2010-08-07T02:33:35.130 | 2010-08-07T02:33:35.130 | null | null | 159 | null |
1383 | 1 | 1594 | null | 0 | 1196 | There's a lot of work done in statistics,
while state-of-art in lossless data compression is apparently this:
[http://mattmahoney.net/dc/dce.html#Section_4](http://mattmahoney.net/dc/dce.html#Section_4)
Please suggest good methods/models applicable for data compression.
To be specific:
1) How to estimate the probabilit... | Suggest a method for statistical data compression | CC BY-SA 2.5 | null | 2010-08-07T03:47:26.330 | 2010-08-14T12:10:58.203 | 2010-08-12T13:39:10.303 | 8 | 799 | [
"modeling",
"compression"
] |
1384 | 2 | null | 1352 | 5 | null |
- I agree with the point that statistics consultants are often brought in later on a project when it's too late to remedy design flaws. It's also true that many statistics books give scant attention to study design issues.
- You say you want designs "preferably for a wide range of methods (e.g. t-test, GLM, GAM, ordi... | null | CC BY-SA 2.5 | null | 2010-08-07T03:55:36.433 | 2010-08-08T11:31:31.680 | 2010-08-08T11:31:31.680 | 183 | 183 | null |
1385 | 1 | 1404 | null | 13 | 3179 | My question is directed to techniques to deal with incomplete data during the classifier/model training/fitting.
For instance, in a dataset w/ a few hundred rows, each row having let's say five dimensions and a class label as the last item, most data points will look like this:
[0.74, 0.39, 0.14, 0.33, 0.34, 0]
A few m... | Techniques for Handling Incomplete/Missing Data | CC BY-SA 2.5 | null | 2010-08-07T05:07:27.083 | 2010-09-16T06:47:49.967 | 2010-09-16T06:47:49.967 | null | 438 | [
"missing-data"
] |
1386 | 1 | 1390 | null | 19 | 10736 | I am trying to test the null $E[X] = 0$, against the local alternative $E[X] > 0$, for a random variable $X$, subject to mild to medium skew and kurtosis of the random variable. Following suggestions by Wilcox in 'Introduction to Robust Estimation and Hypothesis Testing', I have looked at tests based on the trimmed mea... | Robust t-test for mean | CC BY-SA 3.0 | null | 2010-08-07T05:18:58.967 | 2020-10-12T20:08:19.260 | 2012-06-29T05:51:10.347 | 183 | 795 | [
"hypothesis-testing",
"t-test",
"finance",
"robust"
] |
1387 | 2 | null | 1337 | 7 | null | there was the one about the two statisticians who tried to use grant money to pay for their bill at a strip club. They were vindicated when it was explained they were performing a 'posterior analysis'. (groan)
| null | CC BY-SA 2.5 | null | 2010-08-07T06:03:39.740 | 2010-08-07T06:03:39.740 | null | null | 795 | null |
1388 | 2 | null | 1337 | 222 | null | >
A statistician's wife had twins. He
was delighted. He rang the minister
who was also delighted. "Bring them to
church on Sunday and we'll baptize
them," said the minister. "No,"
replied the statistician. "Baptize
one. We'll keep the other as a
control."
STATS: The Magazine For Students of Statistics,... | null | CC BY-SA 2.5 | null | 2010-08-07T07:15:20.137 | 2010-08-07T07:15:20.137 | null | null | null | null |
1389 | 1 | 1392 | null | 5 | 9720 | I came across an error of numerical overflow when running a maximum likelihood estimation on a log-linear specification.
What does numerical overflow mean?
| What is numerical overflow? | CC BY-SA 2.5 | null | 2010-08-07T07:23:07.937 | 2010-08-07T22:38:33.683 | null | null | 189 | [
"estimation",
"maximum-likelihood"
] |
1390 | 2 | null | 1386 | 5 | null | Why are you looking at non-parametric tests? Are the assumptions of the t-test violated? Namely, ordinal or non-normal data and inconstant variances? Of course, if your sample is large enough you can justify the parametric t-test with its greater power despite the lack of normality in the sample. Likewise if your c... | null | CC BY-SA 4.0 | null | 2010-08-07T07:23:55.127 | 2020-10-12T20:08:19.260 | 2020-10-12T20:08:19.260 | 236645 | 485 | null |
1391 | 2 | null | 1386 | 13 | null | I agree that if you want to actually test whether the group means are different (as opposed to testing differences between group medians or trimmed means, etc.), then you don't want to use a nonparametric test that tests a different hypothesis.
- In general p-values from a t-test tend to be fairly accurate given moder... | null | CC BY-SA 2.5 | null | 2010-08-07T07:34:44.433 | 2010-08-07T09:39:58.333 | 2010-08-07T09:39:58.333 | 183 | 183 | null |
1392 | 2 | null | 1389 | 9 | null | It means that the algorithm generated a variable that is greater than the maximum allowed for that type of variable. That is due to the fact that computers use a finite number of bits to represent numbers, so it is not possible to represent ANY number, but only a limited subset of them.
The actual value depends on the ... | null | CC BY-SA 2.5 | null | 2010-08-07T07:35:58.240 | 2010-08-07T07:35:58.240 | null | null | 582 | null |
1393 | 2 | null | 1268 | 6 | null | There is a reasonably new area of research called Matrix Completion, that probably does what you want. A really nice introduction is given in this [lecture](http://videolectures.net/mlss09us_candes_mccota/) by Emmanuel Candes
| null | CC BY-SA 2.5 | null | 2010-08-07T08:12:09.650 | 2010-08-07T09:29:45.423 | 2010-08-07T09:29:45.423 | 352 | 352 | null |
1395 | 1 | 1402 | null | 5 | 1392 | Can anyone recommend me an open source graphic library to create forest and funnel plots?
I was aiming at using it on a Java desktop application.
| Libraries for forest and funnel plots | CC BY-SA 2.5 | 0 | 2010-08-07T13:53:16.873 | 2010-08-11T10:53:09.990 | 2010-08-11T10:53:09.990 | 8 | 807 | [
"data-visualization",
"funnel-plot",
"java"
] |
1396 | 2 | null | 1389 | 3 | null | You can probably avoid your overflow problems by working with the log of the likelihood function rather than the likelihood function itself. Both have the same maximum.
| null | CC BY-SA 2.5 | null | 2010-08-07T14:36:40.593 | 2010-08-07T14:36:40.593 | null | null | 319 | null |
1397 | 2 | null | 1376 | 5 | null | Sure: two answers
a) If by robustness, you mean robust to outliers, then run Hottelling's T-test using a robust estimation of scale/scatter: you will find all the explications and R code here:
[http://www.statsravingmad.com/blog/statistics/a-robust-hotelling-test/](http://www.statsravingmad.com/blog/statistics/a-robust... | null | CC BY-SA 2.5 | null | 2010-08-07T14:40:50.007 | 2010-08-07T15:39:38.530 | 2010-08-07T15:39:38.530 | 603 | 603 | null |
1399 | 1 | 1401 | null | 13 | 2303 | I'm interested in obtaining a bootstrapped confidence interval on quantity X, when this quantity is measured 10 times in each of 10 individuals.
One approach is to obtain the mean per individual, then bootstrap the means (eg. resample the means with replacement).
Another approach is to do the following on each iteratio... | Obtaining and interpreting bootstrapped confidence intervals from hierarchical data | CC BY-SA 2.5 | null | 2010-08-07T18:10:02.220 | 2010-08-23T20:39:21.760 | 2010-08-13T00:57:22.773 | 364 | 364 | [
"confidence-interval",
"bootstrap"
] |
1401 | 2 | null | 1399 | 7 | null | Your first approach is about a between S CI. If you wanted to measure within S then that's the wrong approach.
The second approach would generate a within S CI that would only apply to those 10 individuals.
The last approach is the correct one for the within S CI. Any increases in the CI are because your CI is more re... | null | CC BY-SA 2.5 | null | 2010-08-07T18:31:32.663 | 2010-08-07T18:40:21.040 | 2010-08-07T18:40:21.040 | 601 | 601 | null |
1402 | 2 | null | 1395 | 5 | null | Well, i use [graphviz](http://www.graphviz.org), which has Java bindings [(Grappa](http://www2.research.att.com/~john/Grappa/)).
Although the dot language (graphviz's syntax) is simple, i prefer to use graphviz as a library through the excellent and production-stable python bindings, [pygraphviz](http://networkx.lanl.g... | null | CC BY-SA 2.5 | null | 2010-08-07T21:14:56.077 | 2010-08-11T04:32:31.583 | 2010-08-11T04:32:31.583 | 438 | 438 | null |
1403 | 2 | null | 1389 | 1 | null | As stated by nico, numerical overflow is when computation finds a number that is too great for the limited number of bits allocated by software to store the number. For example, if your software uses 32 bits to store integers, then computing an integer that is greater than 2,147,483,648 (or smaller than -2,147,483,648)... | null | CC BY-SA 2.5 | null | 2010-08-07T22:28:44.363 | 2010-08-07T22:38:33.683 | 2010-08-07T22:38:33.683 | 666 | 666 | null |
1404 | 2 | null | 1385 | 4 | null | I gave this answer to [another question](https://stats.stackexchange.com/questions/1268/svd-dimensionality-reduction-for-time-series-of-different-length/1393#1393), but it might apply here too.
"There is a reasonably new area of research called Matrix Completion, that probably does what you want. A really nice introduc... | null | CC BY-SA 2.5 | null | 2010-08-07T22:57:34.860 | 2010-08-07T22:57:34.860 | 2017-04-13T12:44:51.060 | -1 | 352 | null |
1405 | 1 | null | null | 8 | 20253 | I am attempting to compare two diagnostic odds ratios (DORs). I would like to know of a statistical test which will allow me to do this. Please help! Thank you!
| Statistical test for difference between two odds ratios? | CC BY-SA 2.5 | null | 2010-08-08T00:44:57.630 | 2019-09-12T07:26:51.450 | 2011-04-29T00:28:51.273 | 3911 | null | [
"hypothesis-testing"
] |
1406 | 2 | null | 726 | 39 | null | This is unlikely to be a popular quote, but anyway,
>
If your experiment needs statistics, you ought to have done a better experiment.
Ernest Rutherford
| null | CC BY-SA 2.5 | null | 2010-08-08T04:15:18.393 | 2010-08-08T04:15:18.393 | null | null | 352 | null |
1407 | 2 | null | 1350 | 7 | null | After a day of searching, I found out that the issue was due to starting values. Thought I should just post the answer for future reference.
The frontier command in Stata obtains its starting values using method of moments. The initial values might have produced negative infinity for the log likelihood. To get around ... | null | CC BY-SA 2.5 | null | 2010-08-08T04:50:18.187 | 2010-08-08T04:50:18.187 | null | null | 189 | null |
1408 | 2 | null | 942 | 6 | null | Most commonly used and implemented discrete wavelet basis functions (as distinct from the CWT described in Robin's answer) have two nice properties that make them useful for anomaly detection:
- They're compactly supported.
- They act as band-pass filters with the pass-band determined by their support.
What this me... | null | CC BY-SA 2.5 | null | 2010-08-08T05:55:39.797 | 2010-08-08T19:22:55.600 | 2010-08-08T19:22:55.600 | 61 | 61 | null |
1409 | 2 | null | 1395 | 5 | null | The `rmeta` package in R can produce forest and funnel plots.
[http://cran.r-project.org/web/packages/rmeta/index.html](http://cran.r-project.org/web/packages/rmeta/index.html)
| null | CC BY-SA 2.5 | null | 2010-08-08T11:30:18.780 | 2010-08-08T11:30:18.780 | null | null | 183 | null |
1410 | 2 | null | 570 | 2 | null | I suppose you could do a multidimensional scaling of the correlation or covariance matrix. It's not exactly structural equation modelling, but it might highlight patterns and structure in the correlation or covariance matrix. This could then be formalised with an appropriate model.
| null | CC BY-SA 2.5 | null | 2010-08-08T14:28:10.037 | 2010-08-08T14:28:10.037 | null | null | 183 | null |
1411 | 2 | null | 3 | 15 | null | [ggobi](http://www.ggobi.org/) "is an open source visualization program for exploring high-dimensional data."
Mat Kelcey has a good [5 minute intro to ggobi](http://matpalm.com/blog/2010/06/04/5-minute-ggobi-demo/).
| null | CC BY-SA 2.5 | null | 2010-08-08T14:33:24.690 | 2010-08-08T14:33:24.690 | null | null | 183 | null |
1412 | 1 | 1414 | null | 4 | 1634 | Background: In some cognitive psychology research areas N-alternative forced choice tasks are common. The most common of these is a two alternative forced choice (2AFC). This usually takes the form of participants being given a stimulus and asked to make one of two judgement, e.g. the target stimuli is present/absen... | Consequences of an improper link function in N alternative forced choice procedures (e.g. 2AFC)? | CC BY-SA 2.5 | null | 2010-08-08T18:10:40.850 | 2017-12-21T02:09:02.167 | 2010-11-20T07:29:05.783 | 196 | 196 | [
"logistic",
"link-function"
] |
1413 | 1 | 291134 | null | 7 | 3084 | It seems like the current revision of lmer does not allow for custom link functions.
- If one needs to fit a logistic
linear mixed effect model with a
custom link function what options
are available in R?
- If none - what options are available in other
statistics/programming packages?
- Are there conceptual reason... | Mixed regression models and custom link functions in R? | CC BY-SA 2.5 | null | 2010-08-08T18:14:35.537 | 2017-07-12T10:42:27.217 | null | null | 196 | [
"r",
"regression",
"mixed-model",
"link-function"
] |
1414 | 2 | null | 1412 | 1 | null | My question is this: since chance performance is 50% in a 2AFC trial, is it still reasonable to use the standard logistic link function?
yes.
Think of it this way: suppose you fit a logistic regression where your $y$ variable takes value 1 if subject i has flue, 0 otherwise.
So long as neither $y_i=1$ nor $y_i=0$ are r... | null | CC BY-SA 2.5 | null | 2010-08-08T18:27:45.137 | 2010-08-08T18:35:16.010 | 2010-08-08T18:35:16.010 | 603 | 603 | null |
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